An adaptive tensor voting algorithm combined with texture spectrum

An adaptive tensor voting algorithm combined with texture spectrum is proposed. The image texture spectrum is used to get the adaptive scale parameter of voting field. Then the texture information modifies both the attenuation coefficient and the attenuation field so that we can use this algorithm to create more significant and correct structures in the original image according to the human visual perception. At the same time, the proposed method can improve the edge extraction quality, which includes decreasing the flocculent region efficiently and making image clear. In the experiment for extracting pavement cracks, the original pavement image is processed by the proposed method which is combined with the significant curve feature threshold procedure, and the resulted image displays the faint crack signals submerged in the complicated background efficiently and clearly.

[1]  Gérard G. Medioni,et al.  Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting , 2010, J. Mach. Learn. Res..

[2]  Gérard G. Medioni,et al.  First order tensor voting, and application to 3-D scale analysis , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[3]  Cheng Fang,et al.  Automated kymograph analysis for profiling axonal transport of secretory granules , 2011, Medical Image Anal..

[4]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[5]  P. Dubois,et al.  Local fractal and multifractal features for volumic texture characterization , 2011, Pattern Recognit..

[6]  Chi-Keung Tang,et al.  Tensor voting for image correction by global and local intensity alignment , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[8]  Kwan H. Lee,et al.  Multi-scale tensor voting for feature extraction from unstructured point clouds , 2012, Graph. Model..

[9]  R. Hariharan,et al.  Shape-From-Focus by Tensor Voting , 2012, IEEE Transactions on Image Processing.

[10]  Mandar Kulkarni,et al.  Tensor Voting Based Foreground Object Extraction , 2011, 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics.